{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8e6a8295",
   "metadata": {},
   "source": [
    "### 2.1 使用ChatGPT学习数据基本管理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c4c4e65",
   "metadata": {},
   "source": [
    "#### 2.1.1 数据去重"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3af493f0",
   "metadata": {},
   "source": [
    "使用duplicated()方法可以帮助你找到DataFrame中的重复行。这个方法返回一个布尔Series，指示每行是否是重复的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1eac7fd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   age name\n",
      "5   30   李四\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = {'age': [20, 30, 33, 30, 33, 30],\n",
    "        'name': ['张三', '李四', '王五', '林六', '张三', '李四']}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 识别重复行\n",
    "duplicated_rows = df.duplicated()\n",
    "\n",
    "# 打印包含重复行的数据\n",
    "duplicates = df[duplicated_rows]\n",
    "\n",
    "print(duplicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f747ee5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df数据框 \n",
      "    age name\n",
      "0   20   张三\n",
      "1   30   李四\n",
      "2   33   王五\n",
      "3   30   林六\n",
      "4   33   张三\n",
      "5   30   李四\n",
      "duplicated()方法结果 \n",
      " 0    False\n",
      "1    False\n",
      "2    False\n",
      "3    False\n",
      "4    False\n",
      "5     True\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "# 查看数据框\n",
    "print('df数据框 \\n',df)\n",
    "# 查看duplicated()方法结果\n",
    "print('duplicated()方法结果 \\n',duplicated_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "54744747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4     True\n",
       "5     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 只根据字段name判断是否重复\n",
    "df.duplicated(subset='name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "daf1f8ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除重复记录:\n",
      "    age name\n",
      "0   20   张三\n",
      "1   30   李四\n",
      "2   33   王五\n",
      "3   30   林六\n",
      "4   33   张三\n",
      "查看此时的df:\n",
      "    age name\n",
      "0   20   张三\n",
      "1   30   李四\n",
      "2   33   王五\n",
      "3   30   林六\n",
      "4   33   张三\n",
      "5   30   李四\n"
     ]
    }
   ],
   "source": [
    "# 所有参数为默认值\n",
    "print('删除重复记录:\\n',df.drop_duplicates())\n",
    "print('查看此时的df:\\n',df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "85d97666",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>张三</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>李四</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>王五</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>林六</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age name\n",
       "0   20   张三\n",
       "1   30   李四\n",
       "2   33   王五\n",
       "3   30   林六"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 只要姓名相同就删除，并直接修改df\n",
    "df.drop_duplicates(subset='name',inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a78b226",
   "metadata": {},
   "source": [
    "#### 数据排序"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f16a586",
   "metadata": {},
   "source": [
    "有时候，需要对数据进行排序，以便更进一步分析数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "95228a1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age\n",
       "2  Charlie   22\n",
       "0    Alice   25\n",
       "1      Bob   30\n",
       "3    David   35"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 方法1: sort_values()\n",
    "import pandas as pd\n",
    "\n",
    "# 创建一个示例数据框\n",
    "data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],\n",
    "        'Age': [25, 30, 22, 35]}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 按照 'Age' 列升序排序\n",
    "df_sorted = df.sort_values(by='Age', ascending=True)\n",
    "df_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5b8564fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age\n",
       "3    David   35\n",
       "1      Bob   30\n",
       "0    Alice   25\n",
       "2  Charlie   22"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按照 'Age' 列降序排序\n",
    "df_sorted1 = df.sort_values(by='Age', ascending=False)\n",
    "df_sorted1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "377a4410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B\n",
       "A  1  5\n",
       "B  2  4\n",
       "C  3  6"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 方法2: sort_index()\n",
    "# sort_index() 方法允许你按照索引来排序数据。你可以指定升序或降序排列。以下是一个示例：\n",
    "import pandas as pd\n",
    "\n",
    "# 创建一个示例数据框\n",
    "data = {'A': [3, 1, 2],\n",
    "        'B': [6, 5, 4]}\n",
    "df = pd.DataFrame(data, index=['C', 'A', 'B'])\n",
    "\n",
    "# 按照索引升序排序\n",
    "df_sorted = df.sort_index(ascending=True)\n",
    "df_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "faef02bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B\n",
       "1  1  5\n",
       "3  2  7\n",
       "2  2  4\n",
       "0  3  6"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 方法3: sort_values() 多列排序\n",
    "import pandas as pd\n",
    "\n",
    "# 创建一个示例数据框\n",
    "data = {'A': [3, 1, 2, 2],\n",
    "        'B': [6, 5, 4, 7]}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 按照 'A' 列升序排序，然后按照 'B' 列降序排序\n",
    "df_sorted = df.sort_values(by=['A', 'B'], ascending=[True, False])\n",
    "\n",
    "df_sorted\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19555795",
   "metadata": {},
   "source": [
    "#### 数据连接"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35c8a6a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 创建两个示例数据框\n",
    "data1 = {'Key': ['A', 'B', 'C', 'D'],\n",
    "         'Value1': [1, 2, 3, 4]}\n",
    "df1 = pd.DataFrame(data1)\n",
    "\n",
    "data2 = {'Key': ['B', 'D', 'E', 'F'],\n",
    "         'Value2': [5, 6, 7, 8]}\n",
    "df2 = pd.DataFrame(data2)\n",
    "\n",
    "# 使用 merge 方法将两个数据框合并\n",
    "merged_df = pd.merge(df1, df2, on='Key', how='inner')\n",
    "\n",
    "# 查看合并结果\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc8730be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Key</th>\n",
       "      <th>Value1</th>\n",
       "      <th>Value2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Key  Value1  Value2\n",
       "0   A       1     NaN\n",
       "1   B       2     5.0\n",
       "2   C       3     NaN\n",
       "3   D       4     6.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用 merge 方法将两个数据框按照左连接方式合并\n",
    "merged_df1 = pd.merge(df1, df2, on='Key', how='left')\n",
    "\n",
    "# 查看合并结果\n",
    "merged_df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9486a440",
   "metadata": {},
   "source": [
    "#### 数据分箱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "812f3316",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Age    Age_Group\n",
      "0   25          NaN\n",
      "1   30        Young\n",
      "2   35        Young\n",
      "3   40        Young\n",
      "4   45  Middle-aged\n",
      "5   50  Middle-aged\n",
      "6   55  Middle-aged\n",
      "7   60       Senior\n",
      "8   65       Senior\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 创建一个包含一些示例数据的DataFrame\n",
    "data = pd.DataFrame({'Age': [25, 30, 35, 40, 45, 50, 55, 60, 65]})\n",
    "\n",
    "# 使用cut()方法将'Age'列的数据划分为三个区间\n",
    "bins = [25, 40, 55, 65]\n",
    "labels = ['Young', 'Middle-aged', 'Senior']\n",
    "data['Age_Group'] = pd.cut(data['Age'], bins=bins, labels=labels)\n",
    "\n",
    "# 打印结果\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6d3215ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Age    Age_Group   Age_Group1\n",
      "0   25          NaN        Young\n",
      "1   30        Young        Young\n",
      "2   35        Young        Young\n",
      "3   40        Young        Young\n",
      "4   45  Middle-aged  Middle-aged\n",
      "5   50  Middle-aged  Middle-aged\n",
      "6   55  Middle-aged  Middle-aged\n",
      "7   60       Senior       Senior\n",
      "8   65       Senior       Senior\n"
     ]
    }
   ],
   "source": [
    "# 调整分段区间\n",
    "bins1 = [24, 40, 55, 65]\n",
    "data['Age_Group1'] = pd.cut(data['Age'], bins=bins1, labels=labels)\n",
    "# 打印结果\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42d74458",
   "metadata": {},
   "source": [
    "### 2.2 使用ChatGPT学习描述统计分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13046de9",
   "metadata": {},
   "source": [
    "#### 2.2.2 数据中心趋势分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c8d29668",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "平均值：\n",
      "sepal length (cm)    5.843333\n",
      "sepal width (cm)     3.057333\n",
      "petal length (cm)    3.758000\n",
      "petal width (cm)     1.199333\n",
      "dtype: float64\n",
      "\n",
      "中位数：\n",
      "sepal length (cm)    5.80\n",
      "sepal width (cm)     3.00\n",
      "petal length (cm)    4.35\n",
      "petal width (cm)     1.30\n",
      "dtype: float64\n",
      "\n",
      "第一四分位数：\n",
      "sepal length (cm)    5.1\n",
      "sepal width (cm)     2.8\n",
      "petal length (cm)    1.6\n",
      "petal width (cm)     0.3\n",
      "Name: 0.25, dtype: float64\n",
      "\n",
      "第三四分位数：\n",
      "sepal length (cm)    6.4\n",
      "sepal width (cm)     3.3\n",
      "petal length (cm)    5.1\n",
      "petal width (cm)     1.8\n",
      "Name: 0.75, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "# 加载Iris数据集\n",
    "iris = load_iris()\n",
    "\n",
    "# 创建一个Pandas数据框\n",
    "iris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\n",
    "\n",
    "# 计算每列的平均值\n",
    "mean_values = iris_df.mean()\n",
    "\n",
    "# 计算每列的中位数\n",
    "median_values = iris_df.median()\n",
    "\n",
    "# 计算每列的第一四分位数\n",
    "q1_values = iris_df.quantile(0.25)\n",
    "\n",
    "# 计算每列的第三四分位数\n",
    "q3_values = iris_df.quantile(0.75)\n",
    "\n",
    "print(\"平均值：\")\n",
    "print(mean_values)\n",
    "print(\"\\n中位数：\")\n",
    "print(median_values)\n",
    "print(\"\\n第一四分位数：\")\n",
    "print(q1_values)\n",
    "print(\"\\n第三四分位数：\")\n",
    "print(q3_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1001b38a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
      "mean           5.843333          3.057333              3.758          1.199333\n",
      "50%            5.800000          3.000000              4.350          1.300000\n",
      "25%            5.100000          2.800000              1.600          0.300000\n",
      "75%            6.400000          3.300000              5.100          1.800000\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "# 加载Iris数据集\n",
    "iris = load_iris()\n",
    "\n",
    "# 创建一个Pandas数据框\n",
    "iris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\n",
    "\n",
    "# 使用describe()函数获取统计摘要信息\n",
    "summary = iris_df.describe()\n",
    "\n",
    "# 选择需要的统计指标\n",
    "desired_stats = summary.loc[['mean', '50%', '25%', '75%']]\n",
    "\n",
    "print(desired_stats)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76c2b351",
   "metadata": {},
   "source": [
    "#### 2.2.3 数据离散程度分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29c6e39d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "方差：\n",
      "sepal length (cm)    0.685694\n",
      "sepal width (cm)     0.189979\n",
      "petal length (cm)    3.116278\n",
      "petal width (cm)     0.581006\n",
      "dtype: float64\n",
      "\n",
      "标准差：\n",
      "sepal length (cm)    0.828066\n",
      "sepal width (cm)     0.435866\n",
      "petal length (cm)    1.765298\n",
      "petal width (cm)     0.762238\n",
      "dtype: float64\n",
      "\n",
      "范围：\n",
      "sepal length (cm)    3.6\n",
      "sepal width (cm)     2.4\n",
      "petal length (cm)    5.9\n",
      "petal width (cm)     2.4\n",
      "dtype: float64\n",
      "\n",
      "四分位距 (IQR)：\n",
      "sepal length (cm)    1.3\n",
      "sepal width (cm)     0.5\n",
      "petal length (cm)    3.5\n",
      "petal width (cm)     1.5\n",
      "dtype: float64\n",
      "\n",
      "变异系数：\n",
      "sepal length (cm)    14.171126\n",
      "sepal width (cm)     14.256420\n",
      "petal length (cm)    46.974407\n",
      "petal width (cm)     63.555114\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "# 加载Iris数据集\n",
    "iris = load_iris()\n",
    "data = iris.data\n",
    "\n",
    "# 将数据转换成DataFrame以便于处理\n",
    "df = pd.DataFrame(data, columns=iris.feature_names)\n",
    "\n",
    "# 计算方差\n",
    "variance = df.var()\n",
    "\n",
    "# 计算标准差\n",
    "std_deviation = df.std()\n",
    "\n",
    "# 计算范围\n",
    "data_range = df.max() - df.min()\n",
    "\n",
    "# 计算四分位距 (IQR)\n",
    "Q1 = df.quantile(0.25)\n",
    "Q3 = df.quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# 计算变异系数\n",
    "coefficient_of_variation = (std_deviation / df.mean()) * 100\n",
    "\n",
    "# 打印结果\n",
    "print(\"方差：\")\n",
    "print(variance)\n",
    "print(\"\\n标准差：\")\n",
    "print(std_deviation)\n",
    "print(\"\\n范围：\")\n",
    "print(data_range)\n",
    "print(\"\\n四分位距 (IQR)：\")\n",
    "print(IQR)\n",
    "print(\"\\n变异系数：\")\n",
    "print(coefficient_of_variation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3679147",
   "metadata": {},
   "source": [
    "#### 2.2.4 数据分布形态分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca9c5720",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "偏度: -0.6\n",
      "峰度: -0.7999999999999998\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import skew, kurtosis\n",
    "\n",
    "data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4]\n",
    "\n",
    "skewness = skew(data)\n",
    "kurt = kurtosis(data)\n",
    "\n",
    "print(\"偏度:\", skewness)\n",
    "print(\"峰度:\", kurt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7286b38c",
   "metadata": {},
   "source": [
    "#### 2.2.5 数据频统计分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "860be7c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count数据类型为： <class 'pandas.core.series.Series'>\n",
      "查看频数统计 \n",
      " 0    50\n",
      "1    50\n",
      "2    50\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets  \n",
    "import pandas as pd  \n",
    "  \n",
    "# 加载iris数据集  \n",
    "iris = datasets.load_iris()  \n",
    "target = iris.target  \n",
    "  \n",
    "# 计算每个类别的频数\n",
    "count =  pd.value_counts(target)\n",
    "# 查看count对象及数结果\n",
    "print('count数据类型为：',type(count))\n",
    "print('查看频数统计 \\n',count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a80845fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "类别 0 (setosa): 频数 = 50, 频率 = 0.333\n",
      "类别 1 (versicolor): 频数 = 50, 频率 = 0.333\n",
      "类别 2 (virginica): 频数 = 50, 频率 = 0.333\n"
     ]
    }
   ],
   "source": [
    "# 计算每个类别的频数\n",
    "freq =  pd.value_counts(target,normalize=True) \n",
    "# 打印结果\n",
    "for class_label, count, frequency in zip(np.unique(target), count, freq):\n",
    "    class_name = iris.target_names[class_label]\n",
    "    print(f\"类别 {class_label} ({class_name}): 频数 = {count}, 频率 = {frequency:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afe9e767",
   "metadata": {},
   "source": [
    "### 2.3 使用ChatGPT学习中文文本操作"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b611e4fd",
   "metadata": {},
   "source": [
    "#### 2.3.1 Jieba分词"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7b6c168",
   "metadata": {},
   "source": [
    "利用jieba.cut或jieba.cut_for_search函数可以实现精确模式、全模式或搜索引擎模式的中文分词。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6dc42043",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Dumping model to file cache C:\\Users\\Daniel\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 1.025 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "全模式: 今天/ 交通/ 交通银行/ 银行/ 行客/ 客户/ 客户经理/ 经理/ 邀约/ 之前/ 老客/ 老客户/ 客户/ 了解/ 最新/ 产品/ 。\n",
      "精确模式: 今天/ 交通银行/ 客户经理/ 邀约/ 之前/ 老客户/ 了解/ 最新/ 产品/ 。\n",
      "搜索引擎模式: 今天/ 交通/ 银行/ 交通银行/ 客户/ 经理/ 客户经理/ 邀约/ 之前/ 老客/ 客户/ 老客户/ 了解/ 最新/ 产品/ 。\n"
     ]
    }
   ],
   "source": [
    "import jieba\n",
    "str = '今天交通银行客户经理邀约之前老客户了解最新产品。'\n",
    "# 全模式\n",
    "seg_list = jieba.cut(str, cut_all=True)\n",
    "print(\"全模式: \" + \"/ \".join(seg_list))\n",
    "# 精确模式（默认）\n",
    "seg_list = jieba.cut(str, cut_all=False)\n",
    "print(\"精确模式: \" + \"/ \".join(seg_list))\n",
    "seg_list = jieba.cut_for_search(str)  # 搜索引擎模式\n",
    "print(\"搜索引擎模式: \" +  \"/ \".join(seg_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c296d7e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "今天\n",
      "交通银行\n",
      "客户经理\n",
      "邀约\n",
      "之前\n",
      "老客户\n",
      "了解\n",
      "最新\n",
      "产品\n",
      "。\n"
     ]
    }
   ],
   "source": [
    "seg_list = jieba.cut(str, cut_all=False) # 精确模式\n",
    "for word in seg_list:\n",
    "    print(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dcb169bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用jieba.lcut函数，返回列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d034e369",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['今天', '交通银行', '客户经理', '邀约', '之前', '老客户', '了解', '最新', '产品', '。']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jieba.lcut(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93d4f582",
   "metadata": {},
   "source": [
    "#### 2.3.2 添加自定义词典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5e164330",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "查看未添加自定义词典的分词结果:\n",
      "0    [R, 语言]\n",
      "1     [深度学习]\n",
      "2      [云计算]\n",
      "3     [安全驾驶]\n",
      "Name: str, dtype: object\n",
      "查看添加自定义词典的分词结果:\n",
      "0    [R, 语言]\n",
      "1     [深度学习]\n",
      "2      [云计算]\n",
      "3     [安全驾驶]\n",
      "Name: str, dtype: object\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame({'str':['R语言','深度学习','云计算','安全驾驶']})\n",
    "seg_list = df['str'].apply(lambda x:jieba.lcut(x)) # 精确模式\n",
    "print('查看未添加自定义词典的分词结果:')\n",
    "print(seg_list)\n",
    "jieba.load_userdict('dict/user.txt') # 添加自定义词典\n",
    "seg_list1 = df['str'].apply(lambda x:jieba.lcut(x)) \n",
    "print('查看添加自定义词典的分词结果:')\n",
    "print(seg_list1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5181fdb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     [R语言]\n",
       "1    [深度学习]\n",
       "2     [云计算]\n",
       "3    [安全驾驶]\n",
       "Name: str, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jieba.add_word('R语言') # 添加新词\n",
    "df['str'].apply(lambda x:jieba.lcut(x))  # 精确模式分词"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cacf0f98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [R语言]\n",
       "1    [深度, 学习]\n",
       "2       [云计算]\n",
       "3      [安全驾驶]\n",
       "Name: str, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jieba.del_word('深度学习') # 从词典汇总删除词\n",
    "df['str'].apply(lambda x:jieba.lcut(x))  # 精确模式分词"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "540a37e5",
   "metadata": {},
   "source": [
    "#### 2.3.3 关键词提取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d4cf96cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分词\n",
      "结巴\n",
      "Python\n",
      "中文\n",
      "一款\n"
     ]
    }
   ],
   "source": [
    "import jieba\n",
    "import jieba.analyse\n",
    "\n",
    "# 要提取关键词的中文文本\n",
    "text = \"结巴分词是一款中文分词工具，非常方便和强大。结巴分词基于Python实现。\"\n",
    "\n",
    "# 使用jieba.analyse.extract_tags函数提取关键词\n",
    "keywords = jieba.analyse.extract_tags(text, topK=5, withWeight=False)\n",
    "\n",
    "# 打印提取的关键词\n",
    "for keyword in keywords:\n",
    "    print(keyword)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "18f9cc7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('分词', 2.5078828017)\n",
      "('结巴', 1.4518582905285715)\n",
      "('Python', 0.8539119644928571)\n",
      "('中文', 0.5820075009392857)\n",
      "('一款', 0.5473109304957143)\n"
     ]
    }
   ],
   "source": [
    "# 使用jieba.analyse.extract_tags函数提取关键词\n",
    "keywords = jieba.analyse.extract_tags(text, topK=5, withWeight=True)\n",
    "\n",
    "# 打印提取的关键词\n",
    "for keyword in keywords:\n",
    "    print(keyword)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eaa2da9",
   "metadata": {},
   "source": [
    "#### 2.3.4 词性标注"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4a17ec0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "结巴 : n\n",
      "分词 : n\n",
      "是 : v\n",
      "一款 : m\n",
      "中文 : nz\n",
      "分词 : n\n",
      "工具 : n\n",
      "， : x\n",
      "非常 : d\n",
      "方便 : a\n",
      "和 : c\n",
      "强大 : a\n",
      "。 : x\n"
     ]
    }
   ],
   "source": [
    "import jieba.posseg as pseg\n",
    "\n",
    "# 要进行词性标注的中文文本\n",
    "text = \"结巴分词是一款中文分词工具，非常方便和强大。\"\n",
    "\n",
    "# 使用jieba.posseg.cut函数进行词性标注\n",
    "words = pseg.cut(text)\n",
    "\n",
    "# 打印词性标注结果\n",
    "for word, flag in words:\n",
    "    print(f\"{word} : {flag}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fae7063f",
   "metadata": {},
   "source": [
    "### 2.4 使用ChatGPT学习图像数据操作"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f03d21d",
   "metadata": {},
   "source": [
    "#### 2.4.1 图像读取、显示及保存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "53d92c57",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "# 读取图像\n",
    "image = cv2.imread('data/cat-color.jpg')\n",
    "\n",
    "# 检查图像是否成功加载\n",
    "if image is not None:\n",
    "    # 显示图像\n",
    "    cv2.imshow('Cat Image', image)\n",
    "    \n",
    "    # 等待用户按下任意键后关闭图像窗口\n",
    "    cv2.waitKey(0)\n",
    "    \n",
    "    # 保存图像（可选）\n",
    "    cv2.imwrite('data/cat-color-copy.jpg', image)\n",
    "\n",
    "    # 关闭所有OpenCV窗口\n",
    "    cv2.destroyAllWindows()\n",
    "else:\n",
    "    print('无法加载图像')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "33e86fe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cat-color-copy.jpg', 'cat-color.jpg']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.listdir('data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d6e97a69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_gray = cv2.imread('data/cat-color.jpg',cv2.IMREAD_GRAYSCALE) # 按灰度模式读取图像\n",
    "cv2.imshow('image1',img_gray)\n",
    "cv2.waitKey(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d37bb9e",
   "metadata": {},
   "source": [
    "#### 2.4.2 图像像素的获取和编辑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "74c1a803",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[137 193 242]\n",
      "201\n"
     ]
    }
   ],
   "source": [
    "img = cv2.imread('data/cat-color.jpg') # 彩色图像\n",
    "img_gray = cv2.imread('data/cat-color.jpg',cv2.IMREAD_GRAYSCALE) # 灰色图像\n",
    "# 获取(100,100)处的像素值\n",
    "print(img[100,100])\n",
    "print(img_gray[100,100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b0115b3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patch1 = img[0:200,0:300] # 读取据局部图像\n",
    "cv2.imshow('patch1',patch1)\n",
    "cv2.waitKey(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4de5798e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img[0:50,0:50,0] = 255 # 将所选区域蓝色通道数值修改为255\n",
    "img[0:50,0:50,1] = 0   # 将所选区域红色通道数值修改为0\n",
    "img[0:50,0:50,2] = 0   # 将所选区域绿通道数值修改为0\n",
    "cv2.imshow('blue',img) # 重新绘图\n",
    "cv2.waitKey(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31116725",
   "metadata": {},
   "source": [
    "#### 2.4.3 图像几何变换操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "41b6a4ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 读取原始图像\n",
    "original_image = cv2.imread('data/cat-color.jpg')\n",
    "\n",
    "# 检查图像是否成功加载\n",
    "if original_image is not None:\n",
    "    # 旋转角度（顺时针）\n",
    "    angle = 45\n",
    "\n",
    "    # 计算旋转矩阵\n",
    "    height, width = original_image.shape[:2]\n",
    "    rotation_matrix = cv2.getRotationMatrix2D((width / 2, height / 2), angle, 1)\n",
    "\n",
    "    # 使用旋转矩阵旋转图像\n",
    "    rotated_image = cv2.warpAffine(original_image, rotation_matrix, (width, height))\n",
    "\n",
    "    # 创建一个Matplotlib画布\n",
    "    plt.figure(figsize=(10, 5))\n",
    "\n",
    "    # 在画布上展示原始图像\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.title('Original Image')\n",
    "    plt.imshow(cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB))\n",
    "    plt.axis('off')\n",
    "\n",
    "    # 在画布上展示旋转后的图像\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.title('Rotated Image')\n",
    "    plt.imshow(cv2.cvtColor(rotated_image, cv2.COLOR_BGR2RGB))\n",
    "    plt.axis('off')\n",
    "\n",
    "    # 显示画布\n",
    "    plt.show()\n",
    "else:\n",
    "    print('无法加载图像')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
